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			<h1>infers - v1.4.0</h1>
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				<a href="#infers" id="infers" style="color: inherit; text-decoration: none;">
					<h1>infers</h1>
				</a>
				<p>Machine learning and Matrix operation library by TypeScript.</p>
				<ul>
					<li><a href="https://hans_s.gitee.io/infers">XOR EXAMPLE</a></li>
					<li><a href="https://hans_s.gitee.io/infers/api/">API DOC</a></li>
				</ul>
				<p><img src="https://gitee.com/hans_s/infers/raw/main/docs/net.jpg" alt=""></p>
				<a href="#installed" id="installed" style="color: inherit; text-decoration: none;">
					<h2>Installed</h2>
				</a>
				<p>Make sure NPM is installed, Switch to the project directory then execute the following command.</p>
				<pre><code class="language-shell"><span style="color: #000000">$ npm install infers@latest</span>
</code></pre>
				<p>Reference in project:</p>
				<pre><code class="language-ts"><span style="color: #AF00DB">import</span><span style="color: #000000"> { </span><span style="color: #001080">Matrix</span><span style="color: #000000">, </span><span style="color: #001080">BPNet</span><span style="color: #000000"> } </span><span style="color: #AF00DB">from</span><span style="color: #000000"> </span><span style="color: #A31515">&#039;infers&#039;</span>
</code></pre>
				<a href="#examples" id="examples" style="color: inherit; text-decoration: none;">
					<h2>Examples</h2>
				</a>
				<p>Matrix transpose: </p>
				<pre><code class="language-ts"><span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">m</span><span style="color: #000000"> = </span><span style="color: #0000FF">new</span><span style="color: #000000"> </span><span style="color: #795E26">Matrix</span><span style="color: #000000">([</span>
<span style="color: #000000">  [</span><span style="color: #098658">1</span><span style="color: #000000">, </span><span style="color: #098658">5</span><span style="color: #000000">, </span><span style="color: #098658">0</span><span style="color: #000000">],</span>
<span style="color: #000000">  [</span><span style="color: #098658">2</span><span style="color: #000000">, </span><span style="color: #098658">4</span><span style="color: #000000"> , -</span><span style="color: #098658">1</span><span style="color: #000000">],</span>
<span style="color: #000000">  [</span><span style="color: #098658">0</span><span style="color: #000000">, -</span><span style="color: #098658">2</span><span style="color: #000000">, </span><span style="color: #098658">0</span><span style="color: #000000">]</span>
<span style="color: #000000">])</span>
<span style="color: #001080">m</span><span style="color: #000000">.</span><span style="color: #0070C1">T</span><span style="color: #000000">.</span><span style="color: #795E26">print</span><span style="color: #000000">()</span>
<span style="color: #008000">// Matrix 3x3 [</span>
<span style="color: #008000">//  1, 2, 0, </span>
<span style="color: #008000">//  5, 4, -2, </span>
<span style="color: #008000">//  0, -1, 0, </span>
<span style="color: #008000">// ]</span>
</code></pre>
				<p>BP neural network example of XOR, three-layer network: </p>
				<pre><code class="language-ts"><span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">xs</span><span style="color: #000000"> = </span><span style="color: #0000FF">new</span><span style="color: #000000"> </span><span style="color: #795E26">Matrix</span><span style="color: #000000">([[</span><span style="color: #098658">1</span><span style="color: #000000">, </span><span style="color: #098658">0</span><span style="color: #000000">], [</span><span style="color: #098658">0</span><span style="color: #000000">, </span><span style="color: #098658">1</span><span style="color: #000000">], [</span><span style="color: #098658">0</span><span style="color: #000000">, </span><span style="color: #098658">0</span><span style="color: #000000">], [</span><span style="color: #098658">1</span><span style="color: #000000">, </span><span style="color: #098658">1</span><span style="color: #000000">]])</span>
<span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">ys</span><span style="color: #000000"> = </span><span style="color: #0000FF">new</span><span style="color: #000000"> </span><span style="color: #795E26">Matrix</span><span style="color: #000000">([[</span><span style="color: #098658">1</span><span style="color: #000000">], [</span><span style="color: #098658">1</span><span style="color: #000000">], [</span><span style="color: #098658">0</span><span style="color: #000000">], [</span><span style="color: #098658">0</span><span style="color: #000000">]])</span>
<span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">model</span><span style="color: #000000"> = </span><span style="color: #0000FF">new</span><span style="color: #000000"> </span><span style="color: #795E26">BPNet</span><span style="color: #000000">([</span><span style="color: #098658">2</span><span style="color: #000000">, [</span><span style="color: #098658">6</span><span style="color: #000000">, </span><span style="color: #A31515">&#039;Tanh&#039;</span><span style="color: #000000">], [</span><span style="color: #098658">1</span><span style="color: #000000">, </span><span style="color: #A31515">&#039;Sigmoid&#039;</span><span style="color: #000000">]], { </span><span style="color: #001080">rate:</span><span style="color: #000000"> </span><span style="color: #098658">0.1</span><span style="color: #000000"> })</span>
<span style="color: #001080">model</span><span style="color: #000000">.</span><span style="color: #795E26">fit</span><span style="color: #000000">(</span><span style="color: #001080">xs</span><span style="color: #000000">, </span><span style="color: #001080">ys</span><span style="color: #000000">, {</span>
<span style="color: #000000">  </span><span style="color: #001080">epochs:</span><span style="color: #000000"> </span><span style="color: #098658">5000</span><span style="color: #000000">, </span><span style="color: #795E26">onEpoch</span><span style="color: #001080">:</span><span style="color: #000000"> (</span><span style="color: #001080">epoch</span><span style="color: #000000">, </span><span style="color: #001080">loss</span><span style="color: #000000">) </span><span style="color: #0000FF">=&gt;</span><span style="color: #000000"> {</span>
<span style="color: #000000">    </span><span style="color: #AF00DB">if</span><span style="color: #000000"> (</span><span style="color: #001080">epoch</span><span style="color: #000000"> % </span><span style="color: #098658">100</span><span style="color: #000000"> === </span><span style="color: #098658">0</span><span style="color: #000000">) </span><span style="color: #001080">console</span><span style="color: #000000">.</span><span style="color: #795E26">log</span><span style="color: #000000">(</span><span style="color: #A31515">&#039;epoch:&#039;</span><span style="color: #000000"> + </span><span style="color: #001080">epoch</span><span style="color: #000000">, </span><span style="color: #A31515">&#039;loss:&#039;</span><span style="color: #000000">, </span><span style="color: #001080">loss</span><span style="color: #000000">)</span>
<span style="color: #000000">  }</span>
<span style="color: #000000">})</span>
<span style="color: #001080">model</span><span style="color: #000000">.</span><span style="color: #795E26">predict</span><span style="color: #000000">(</span><span style="color: #001080">xs</span><span style="color: #000000">).</span><span style="color: #795E26">print</span><span style="color: #000000">()</span>
<span style="color: #008000">// Matrix 4x1 [</span>
<span style="color: #008000">//  0.9862025352830867, </span>
<span style="color: #008000">//  0.986128496195502, </span>
<span style="color: #008000">//  0.01443800549676924, </span>
<span style="color: #008000">//  0.014425871504885788, </span>
<span style="color: #008000">// ]</span>
</code></pre>
				<p>BP neural network example of addition, four-layer network: </p>
				<pre><code class="language-ts"><span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">xs</span><span style="color: #000000"> = </span><span style="color: #0000FF">new</span><span style="color: #000000"> </span><span style="color: #795E26">Matrix</span><span style="color: #000000">([[</span><span style="color: #098658">1</span><span style="color: #000000">, </span><span style="color: #098658">4</span><span style="color: #000000">], [</span><span style="color: #098658">3</span><span style="color: #000000">, </span><span style="color: #098658">2</span><span style="color: #000000">], [</span><span style="color: #098658">6</span><span style="color: #000000">, </span><span style="color: #098658">5</span><span style="color: #000000">], [</span><span style="color: #098658">4</span><span style="color: #000000">, </span><span style="color: #098658">7</span><span style="color: #000000">]])</span>
<span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">ys</span><span style="color: #000000"> = </span><span style="color: #0000FF">new</span><span style="color: #000000"> </span><span style="color: #795E26">Matrix</span><span style="color: #000000">([[</span><span style="color: #098658">5</span><span style="color: #000000">], [</span><span style="color: #098658">5</span><span style="color: #000000">], [</span><span style="color: #098658">11</span><span style="color: #000000">], [</span><span style="color: #098658">11</span><span style="color: #000000">]])</span>
<span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">model</span><span style="color: #000000"> = </span><span style="color: #0000FF">new</span><span style="color: #000000"> </span><span style="color: #795E26">BPNet</span><span style="color: #000000">([</span><span style="color: #098658">2</span><span style="color: #000000">, </span><span style="color: #098658">6</span><span style="color: #000000">, </span><span style="color: #098658">6</span><span style="color: #000000">, </span><span style="color: #098658">1</span><span style="color: #000000">], { </span><span style="color: #001080">mode:</span><span style="color: #000000"> </span><span style="color: #A31515">&#039;bgd&#039;</span><span style="color: #000000">, </span><span style="color: #001080">rate:</span><span style="color: #000000"> </span><span style="color: #098658">0.01</span><span style="color: #000000"> })</span>
<span style="color: #001080">model</span><span style="color: #000000">.</span><span style="color: #795E26">fit</span><span style="color: #000000">(</span><span style="color: #001080">xs</span><span style="color: #000000">, </span><span style="color: #001080">ys</span><span style="color: #000000">, {</span>
<span style="color: #000000">  </span><span style="color: #001080">epochs:</span><span style="color: #000000"> </span><span style="color: #098658">500</span><span style="color: #000000">, </span><span style="color: #795E26">onEpoch</span><span style="color: #001080">:</span><span style="color: #000000"> (</span><span style="color: #001080">epoch</span><span style="color: #000000">, </span><span style="color: #001080">loss</span><span style="color: #000000">) </span><span style="color: #0000FF">=&gt;</span><span style="color: #000000"> {</span>
<span style="color: #000000">    </span><span style="color: #001080">console</span><span style="color: #000000">.</span><span style="color: #795E26">log</span><span style="color: #000000">(</span><span style="color: #A31515">&#039;epoch:&#039;</span><span style="color: #000000"> + </span><span style="color: #001080">epoch</span><span style="color: #000000">, </span><span style="color: #A31515">&#039;loss:&#039;</span><span style="color: #000000">, </span><span style="color: #001080">loss</span><span style="color: #000000">)</span>
<span style="color: #000000">  }</span>
<span style="color: #000000">})</span>
<span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">xs2</span><span style="color: #000000"> = </span><span style="color: #0000FF">new</span><span style="color: #000000"> </span><span style="color: #795E26">Matrix</span><span style="color: #000000">([[</span><span style="color: #098658">5</span><span style="color: #000000">, </span><span style="color: #098658">8</span><span style="color: #000000">], [</span><span style="color: #098658">22</span><span style="color: #000000">, </span><span style="color: #098658">6</span><span style="color: #000000">], [-</span><span style="color: #098658">5</span><span style="color: #000000">, </span><span style="color: #098658">9</span><span style="color: #000000">], [-</span><span style="color: #098658">5</span><span style="color: #000000">, -</span><span style="color: #098658">4</span><span style="color: #000000">]])</span>
<span style="color: #001080">model</span><span style="color: #000000">.</span><span style="color: #795E26">predict</span><span style="color: #000000">(</span><span style="color: #001080">xs2</span><span style="color: #000000">).</span><span style="color: #795E26">print</span><span style="color: #000000">()</span>
<span style="color: #008000">// Matrix 4x1 [</span>
<span style="color: #008000">//  12.994745740521667, </span>
<span style="color: #008000">//  27.99134620596921, </span>
<span style="color: #008000">//  3.9987224114576856, </span>
<span style="color: #008000">//  -9.000000644547901,</span>
<span style="color: #008000">// ]</span>
</code></pre>
				<p>RNN: Recurrent neural network example:</p>
				<pre><code class="language-ts"><span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">trainData</span><span style="color: #000000"> = [</span><span style="color: #A31515">&#039;hello rnn&#039;</span><span style="color: #000000">, </span><span style="color: #A31515">&#039;good morning&#039;</span><span style="color: #000000">, </span><span style="color: #A31515">&#039;I love 🍎!&#039;</span><span style="color: #000000">, </span><span style="color: #A31515">&#039;I eat 🍊!&#039;</span><span style="color: #000000">]</span>
<span style="color: #0000FF">let</span><span style="color: #000000"> </span><span style="color: #001080">net</span><span style="color: #000000"> = </span><span style="color: #0000FF">new</span><span style="color: #000000"> </span><span style="color: #795E26">RNN</span><span style="color: #000000">({ </span><span style="color: #001080">trainData</span><span style="color: #000000"> })</span>
<span style="color: #001080">net</span><span style="color: #000000">.</span><span style="color: #795E26">fit</span><span style="color: #000000">({</span>
<span style="color: #000000">  </span><span style="color: #001080">epochs:</span><span style="color: #000000"> </span><span style="color: #098658">1500</span><span style="color: #000000">, </span><span style="color: #795E26">onEpochs</span><span style="color: #001080">:</span><span style="color: #000000"> (</span><span style="color: #001080">epoch</span><span style="color: #000000">, </span><span style="color: #001080">loss</span><span style="color: #000000">) </span><span style="color: #0000FF">=&gt;</span><span style="color: #000000"> {</span>
<span style="color: #000000">    </span><span style="color: #AF00DB">if</span><span style="color: #000000"> (</span><span style="color: #001080">epoch</span><span style="color: #000000"> % </span><span style="color: #098658">10</span><span style="color: #000000"> === </span><span style="color: #098658">0</span><span style="color: #000000">) </span><span style="color: #001080">console</span><span style="color: #000000">.</span><span style="color: #795E26">log</span><span style="color: #000000">(</span><span style="color: #A31515">&#039;epoch: &#039;</span><span style="color: #000000">, </span><span style="color: #001080">epoch</span><span style="color: #000000">, </span><span style="color: #A31515">&#039;loss: &#039;</span><span style="color: #000000">, </span><span style="color: #001080">loss</span><span style="color: #000000">)</span>
<span style="color: #000000">  }</span>
<span style="color: #000000">})</span>
<span style="color: #001080">console</span><span style="color: #000000">.</span><span style="color: #795E26">log</span><span style="color: #000000">(</span><span style="color: #001080">net</span><span style="color: #000000">.</span><span style="color: #795E26">predict</span><span style="color: #000000">(</span><span style="color: #A31515">&#039;I love&#039;</span><span style="color: #000000">))</span>
<span style="color: #001080">console</span><span style="color: #000000">.</span><span style="color: #795E26">log</span><span style="color: #000000">(</span><span style="color: #001080">net</span><span style="color: #000000">.</span><span style="color: #795E26">predict</span><span style="color: #000000">(</span><span style="color: #A31515">&#039;I eat&#039;</span><span style="color: #000000">))</span>
<span style="color: #001080">console</span><span style="color: #000000">.</span><span style="color: #795E26">log</span><span style="color: #000000">(</span><span style="color: #001080">net</span><span style="color: #000000">.</span><span style="color: #795E26">predict</span><span style="color: #000000">(</span><span style="color: #A31515">&#039;hel&#039;</span><span style="color: #000000">))</span>
<span style="color: #001080">console</span><span style="color: #000000">.</span><span style="color: #795E26">log</span><span style="color: #000000">(</span><span style="color: #001080">net</span><span style="color: #000000">.</span><span style="color: #795E26">predict</span><span style="color: #000000">(</span><span style="color: #A31515">&#039;good&#039;</span><span style="color: #000000">))</span>
<span style="color: #008000">//  🍊!/n</span>
<span style="color: #008000">//  🍎!/n</span>
<span style="color: #008000">// lo rnn/n</span>
<span style="color: #008000">//  morning/n</span>
</code></pre>
				<a href="#api" id="api" style="color: inherit; text-decoration: none;">
					<h2>API</h2>
				</a>
				<ul>
					<li><strong>NetShape</strong>: <code>[number, (number | [number, ActivationFunction]), ...(number | [number, ActivationFunction])[]]</code><br>The hierarchical structure of the network model, It includes the number of neurons in each layer, the type of activation function and the total number of layers.</li>
					<li><strong>rate</strong>: <code>number</code><br>The learning rate is the update step of every gradient descent, generally between 0 and 1.</li>
					<li><strong>epochs</strong>: <code>number</code><br>All the data of the whole training set are iterated once.</li>
					<li><strong>ActivationFunction</strong>: <code>&#39;Sigmoid&#39; | &#39;Relu&#39; | &#39;Tanh&#39; | &#39;Softmax&#39;</code></li>
					<li><strong>Mode</strong>: <code>&#39;sgd&#39; | &#39;bgd&#39; | &#39;mbgd&#39;</code></li>
				</ul>
				<p>Different learning rates, iterations and network shapes are needed to deal with different problems, which need to be adjusted according to the cost function. Parameter optimization is also the process of model optimization.</p>
				<a href="#export" id="export" style="color: inherit; text-decoration: none;">
					<h2>Export</h2>
				</a>
				<ul>
					<li>class Matrix<ul>
							<li>Mathematical operation of matrix</li>
							<li>addition, multiply, transpose, determinant, inverse</li>
						</ul>
					</li>
					<li>class BPNet<ul>
							<li>Fully connected neural network</li>
							<li>Multi-layer network model</li>
						</ul>
					</li>
					<li>class RNN<ul>
							<li>Recurrent neural network</li>
							<li>Used natural language processing</li>
						</ul>
					</li>
				</ul>
			</div>
		</div>
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